arXiv subjects

Ling Xia

Publications and source records attributed to Ling Xia.

4 recordsLinked to original sources

arXiv2026

AI-Driven SERS for Non-invasive and Label-Free Extracellular Vesicle Detection Across Cellular Origins in Tears and Sweat

Wearable sensing technology capable of point-of-care, continuous and non-invasive analysis of exosomes in biofluid such as tears and sweat is an essential part for future personalized medicine. Major detection and identification methods of cell secreted Extracellular Vesicles (EVs) often require labeling and are time-consuming, resulting in low efficiency in EV mechanism research and disease diagnosis. While the label-free Surface-enhanced Raman spectroscopy (SERS) has been combined with deep learning model for EV identification in blood, their application to non-invasive detection of EVs in tears and sweat are missing. Here, we filled this gap by developing an artificial intelligence (AI)-assisted Surface-enhanced Raman spectroscopy (SERS) method based on salt-induced nanoparticle aggregation for fast EV identification in tears and sweat with high accuracy. Significantly, our label-free detection and AI differentiation of EVs from 6 cell lines (HepG2, Hela, 143B, LO-2, BMSC, H8) achieved the identification of EVs in tear fluids from 7 different disease sources with accuracies >92%. Our results showed that this platform can not only distinguish EVs from multiple cell sources but also generate highly reproducible and selective EV signals in tear fluids without a need for chemical labeling or separation steps. Molecular dynamics simulations revealed that silver atoms (Ag) form electrostatic interactions with oxygen atoms of multiple amino acid residues in proteins, suggesting a high affinity. This strategy realizes ultra-sensitive and anti-interference detection of EVs, providing a new idea for the rapid diagnosis of clinical diseases.

cond-mat.mes-hall↗

arXiv2025

Joint inversion for Vp, Vp/Vs of the San Fransico Bay Area using ADTomo

This article presents a new seismological tomography method based on the fast sweeping method and advanced seismic phase picking techniques to study the complex geological structures of the San Francisco Bay Area. By calculating the eikonal equation using the fast-sweeping method, this study obtains travel time information and gradient data under a given velocity structure. With an automatic differentiation algorithm to calculate gradients of the loss function and the L-BFGS algorithm to achieve optimization, the velocity model is iteratively adjusted to minimize the loss function. The P wave to S wave velocity ratio obtained through joint inversion is more reliable than the velocity ratio obtained directly by dividing the P wave and S wave velocity models. Compared to traditional inversion, the velocity ratio here does not require the same P wave and S wave travel path, thus improving the accuracy of the velocity ratio. The method was applied to the San Francisco Bay Area, a region with complex geological structures and significant seismic activities. This region, a focal point of research interest, also provides abundant seismic data for this study. We use deep learning methods to automatically pick seismic phases, acquiring abundant P wave and S wave travel time information. This project obtains the S wave velocity structure and P wave to S wave velocity ratio results for the first time. Compared to previous inversion results, the P wave velocity model here demonstrates higher resolution and better geological correspondence. High and low velocity anomalies also align well with geological maps, providing reasonable explanations in terms of lithology.

physics.geo-ph↗

arXiv2024

Quasihyperbolic metric and Gromov hyperbolic spaces I

In this paper, we introduce the concepts of short arc and length map in quasihyperbolic metric spaces, and obtain some geometric characterizations of Gromov hyperbolicity for quasihyperbolic metric spaces in terms of the properties of short arc and length map.

math.MG↗

arXiv2022

3D-EPI Blip-Up/Down Acquisition (BUDA) with CAIPI and Joint Hankel Structured Low-Rank Reconstruction for Rapid Distortion-Free High-Resolution T2* Mapping

Purpose: This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitative T2* mapping. Methods: 3D-Blip-Up and -Down Acquisition (3D-BUDA) sequence is designed for both single- and multi-echo 3D GRE-EPI imaging using multiple shots with blip-up and -down readouts to encode B0 field map information. Complementary k-space coverage is achieved using controlled aliasing in parallel imaging (CAIPI) sampling across the shots. For image reconstruction, an iterative hard-thresholding algorithm is employed to minimize the cost function that combines field map information informed parallel imaging with the structured low-rank constraint for multi-shot 3D-BUDA data. Extending 3D-BUDA to multi-echo imaging permits T2* mapping. For this, we propose constructing a joint Hankel matrix along both echo and shot dimensions to improve the reconstruction. Results: Experimental results on in vivo multi-echo data demonstrate that, by performing joint reconstruction along with both echo and shot dimensions, reconstruction accuracy is improved compared to standard 3D-BUDA reconstruction. CAIPI sampling is further shown to enhance the image quality. For T2* mapping, T2* values from 3D-Joint-CAIPI-BUDA and reference multi-echo GRE are within limits of agreement as quantified by Bland-Altman analysis. Conclusions: The proposed technique enables rapid 3D distortion-free high-resolution imaging and T2* mapping. Specifically, 3D-BUDA enables 1-mm isotropic whole-brain imaging in 22 s at 3 T and 9 s on a 7 T scanner. The combination of multi-echo 3D-BUDA with CAIPI acquisition and joint reconstruction enables distortion-free whole-brain T2* mapping in 47 s at 1.1x1.1x1.0 mm3 resolution.

eess.IV↗